The general workflow of a current face recognition system is divided into three parts. The first part is to select a facial image suitable for recognition from a video stream; the second part is to capture feature points of the facial image; and the third part is to perform a one-to-one or one-to-many comparison on the feature points of the facial image and the captured features of a plurality of specific persons in a database of the face recognition system. When the most similar facial features are obtained, a recognition result can be generated.
In addition, in the first part above, the facial image used for performing recognition is usually selected from a video. Most of current image-selecting methods use a face detection and analysis module (a face detector) to filter out a facial image with a specific face pose (such as, a front face image). This method uses the face pose information as a condition for screening the facial images. However, if the face region of the facial image is blocked by some shelters (such as, sunglasses or scarves) or has different facial expression changes, the shelters and facial expression changes will affect the final recognition result, which may reduce the recognition rate of the face recognition system.
Hence, how to select a facial image suitable for face comparison to improve the recognition rate of one-to-one or one-to-many comparison of a face recognition system has become an important topic for the person skilled in the art.